Drugs Analysis and Biomedicine Applications

Background

In recent years, the integration of biomedical engineering with pharmacological research has become increasingly important. Traditional drug evaluation methods often rely on behavioral assessments or biochemical markers; however, physiological signals such as EEG provide a direct and non-invasive window into brain function. Changes in neural activity induced by pharmacological treatments or substance use can be effectively captured and quantified using advanced signal-processing techniques.

The rapid development of machine learning algorithms has further enhanced the ability to classify, model, and predict drug-related effects from biomedical data. These approaches support a deeper understanding of drug mechanisms, treatment monitoring, and personalized medicine, making them highly relevant for modern biomedicine.

The symposium, which serves as a specialized session of the 4th International Conference on Environmental Geoscience and Earth Ecology (ICEGEE 2026), will focus on drugs and biomedicine.

Goal/Rationale

The primary goal of this workshop is to explore how biomedical signal analysis can be used to assess and interpret the effects of drugs within a biomedicine framework. Despite significant progress, challenges remain in developing reliable, objective, and non-invasive tools for evaluating pharmacological impacts on brain function. By bringing together researchers and practitioners from biomedical engineering, neuroscience, and data science, this workshop aims to:

• Discuss advanced methodologies for analyzing drug-related changes in EEG signals
• Highlight machine learning-based approaches for classification and prediction
• Explore clinical and biomedical applications of these techniques
• Foster collaboration and inspire future interdisciplinary research

Scope

The scope of this workshop includes, but is not limited to, the following topics:

• Biomedical Signal Processing for Drug Analysis: Advanced techniques for analyzing EEG and other physiological signals affected by pharmacological interventions
• Machine Learning in Biomedicine: Classification, prediction, and pattern recognition of drug-induced changes
• EEG-Based Assessment of Pharmacological Effects: Feature extraction, time-frequency analysis, and connectivity measures
• Substance Dependency and Brain Dynamics: Signal-based analysis of dependency-related neural alterations
• Clinical and Biomedical Applications: Case studies demonstrating real-world applications in healthcare and research
• Future Perspectives: Emerging trends in drug analysis, biomedicine, and personalized treatment monitoring

Participants are encouraged to contribute original research, review studies, and application-oriented work related to drug analysis and biomedical signal evaluation.